Querying and analyzing biological data with BioGraph
- Published
- Accepted
- Subject Areas
- Bioinformatics, Data Science
- Keywords
- Bioinformatics database, Integrated database, Graph database, On-line tool, Web application, Biological data
- Copyright
- © 2017 Messina et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Querying and analyzing biological data with BioGraph. PeerJ Preprints 5:e3309v1 https://doi.org/10.7287/peerj.preprints.3309v1
Abstract
Nowadays, a huge amount of biomedical data of different biological entities is provided by many online databases and services, each with its own data model, user interface and query language. However, typical bioinformatics scenarios require the use of more than one resource. Therefore, the availability of a single bioinformatics platform that integrates many biological resources and services is a fundamental issue.
Some attempts to go beyond the drawbacks of a handcrafted combination of different resources have already been made. Regardless of focus and considered biological data sources, existing tools lack one or more high-level functional features, such as web interface availability, dynamic data visualization, custom queries support, analytics, and expandability.
Here, we present BioGraph, a web application that allows to query, visualize and analyze biological data belonging to several online available resources.
BioGraph is built on top of our previously developed graph database called BioGraphDB, which collects and integrates heterogeneous biological data and makes them available through a common structure and Gremlin as unique query language.
BioGraph makes use of state-of-the-art technologies and provides some pre-compiled bioinformatics scenarios, as well as the possibility to perform custom queries and obtaining an interactive and dynamic visualization of results.
Author Comment
This is an abstract which has been accepted for the NETTAB 2017 Workshop